#0: Fix dummy weight issue

models/demos/llama3/tt/model_config.py

@@ -161,6 +161,20 @@ def __init__(
             self._set_model_params(self.DEFAULT_CKPT_DIR)
         else:  # With Dummy weights, set the params from the local copy inside the model folder. This is required for CI pipeline that doesn't mount the external folders.
             self.checkpoint_type = CheckpointType.Meta
+            if "3.2-1B" in self.DEFAULT_CKPT_DIR:
+                local_params = "LLAMA3_2_1B_PARAMS"
+            elif "3.2-3B" in self.DEFAULT_CKPT_DIR:
+                local_params = "LLAMA3_2_3B_PARAMS"
+            elif "3.1-8B" in self.DEFAULT_CKPT_DIR:
+                local_params = "LLAMA3_1_8B_PARAMS"
+            elif "3.2-11B" in self.DEFAULT_CKPT_DIR:
+                local_params = "LLAMA3_2_11B_PARAMS"
+            elif "3.1-70B" in self.DEFAULT_CKPT_DIR:
+                local_params = "LLAMA3_1_70B_PARAMS"
+            else:
+                raise ValueError(
+                    f"No local params found for {self.DEFAULT_CKPT_DIR}, dummy weights are not supported for this model"
+                )
             self._set_model_params(self.LOCAL_LLAMA_PARAMS[local_params])
 
         # Set the max number of tokens for each prefill chunk based on the model and device
@@ -1069,31 +1083,25 @@ def _set_llama_params(self, checkpoint_dir):
         # Set the model name based on the checkpoint directory being loaded
         # FIXME: add a llama prefix to all llama-specific models and names
         if "3.2-1B" in checkpoint_dir:
-            local_params = "LLAMA3_2_1B_PARAMS"
             self.model_name = "Llama3.2-1B" + "-Instruct" if self.instruct else ""
             self.rope_scaling_factor = 32
         elif "3.2-3B" in checkpoint_dir:
-            local_params = "LLAMA3_2_3B_PARAMS"
             self.model_name = "Llama3.2-3B" + "-Instruct" if self.instruct else ""
             self.rope_scaling_factor = 32
         elif "3.1-8B" in checkpoint_dir:
-            local_params = "LLAMA3_1_8B_PARAMS"
             self.model_name = "Llama3.1-8B" + "-Instruct" if self.instruct else ""
             self.rope_scaling_factor = 8
         elif "3.2-11B" in checkpoint_dir:
-            local_params = "LLAMA3_2_11B_PARAMS"
             self.model_name = "Llama3.2-11B" + "-Instruct" if self.instruct else ""
             self.rope_scaling_factor = 8  # shared with 3.1-8B
         elif "3.1-70B" in checkpoint_dir:
-            local_params = "LLAMA3_1_70B_PARAMS"
             self.model_name = "Llama3.1-70B" + "-Instruct" if self.instruct else ""
             self.rope_scaling_factor = 8
             self.is_70b = True  # self.dim == 8192 and self.n_layers == 80
         else:
-            local_params = "UNKNOWN"
             self.model_name = "Unknown"
             self.rope_scaling_factor = 4
-            logger.warning(f"Unknown model: {LLAMA_DIR}")
+            logger.warning(f"Unknown Meta-style model: {checkpoint_dir}")
         self.orig_context_len = 8192
 
     def _set_hf_params(self, checkpoint_dir):